Carbon emissions from a temperate coastal peatland wildfire: contributions from natural plant communities and organic soils

Background One of the scientific challenges of understanding climate change has been determining the important drivers and metrics of global carbon (C) emissions and C cycling in tropical, subtropical, boreal, subarctic, and temperate peatlands. Peatlands account for 3% of global land cover, yet contain a major reservoir of 550 gigatons (Gt) of soil C, and serve as C sinks for 0.37 Gt of carbon dioxide (CO2) a year. In the United States, temperate peatlands are estimated to store 455 petagrams of C (PgC). There has been increasing interest in the role of wildfires in C cycling and altering peatlands from C sinks to major C sources. We estimated above- and below-ground C emissions from the Pains Bay Fire, a long-duration wildfire (112 days; 18,329 ha) that burned a coastal peatland in eastern North Carolina, USA. Results Soil C emissions were estimated from pre- and post-burn Light Detection and Ranging (LIDAR) soil elevation data, soils series and C content mapping, remotely sensed soil burn severity, and post-burn field surveys of soil elevation. Total above-ground C emissions from the fire were 2,89,579 t C and 214 t C ha−1 for the 10 vegetation associations within the burn area perimeter. Above-ground sources of C emissions were comprised of litter (69,656 t C), shrub (1,68,983 t C), and foliage (50,940 t C). Total mean below-ground C emissions were 5,237,521 t C, and ranged from 2,630,529 to 8,287,900 t C, depending on organic matter content of different soil horizons within each of the 7 soil series. The mean below-ground C emissions within the burn area were 1,595.6 t C ha−1 and ranged from 629.3 to 2511.3 t C ha−1. Conclusions In contrast to undisturbed temperate peatlands, human induced disturbances of the natural elevation gradient of the peatland has resulted in increased heterogeneity of floristic variation and assemblages that are a product of the spatial and temporal patterns of the water table level and the surface wetness across peatlands. Human induced changes in surface hydrology and land use influenced the fuel characteristics of natural vegetation and associated soils, thus influencing wildfire risk, behavior, and the resulting C emissions.

Carbon Emissions from A Temperate Coastal Peatland Wildfire: Contributions from Natural Plant Communities and Organic Soils

Background: One of the scientific challenges of understanding climate change has been determining the important drivers and metrics ofglobal carbon (C) emissions and C cycling in tropical, subtropical, boreal, subarctic, and temperate peatlands. Peatlands account for 3% of global land cover, yet contain a major reservoir of 550 gigatons (Gt) of soil C, and serve as C sinks for 0.37 Gt of carbon dioxide (CO2) a year. In the United States, temperate peatlands areestimated to store 455 petagrams of C (PgC).There has been increasing interest in the role of wildfires in C cycling and altering peatlands from C sinks to major C sources. We estimated above- andbelow-ground C emissions from the Pains Bay Fire, a long-duration wildfire (112 days; 18,329 ha)that burned a coastal peatlandin eastern North Carolina, USA. Results: Soil C emissions were estimated from pre- and post-burn Light Detectionand Ranging (LIDAR) soil elevation data,soils series and C content mapping, remotely sensed soilburn severity, and post-burn field surveys of soil elevation.Total above-ground C emissions from the fire were 289,579 tC and 214 t C ha-1for the 10 vegetation communitieswithin the burn area perimeter. Above-ground sources of C emissions were comprised of litter (69,656 t C), shrub (168,983 t C), and foliage (50,940 t C).Total mean below-ground C emissions were 5,237,521 t C, and ranged from 2,630,529 – 8,287,900 t C,depending on organic matter content of different soil horizonswithin each of the 7 soil series. The mean below-ground C emissions within the burn area were 1,595.6 t C ha-1 and rangedfrom 629.3 – 2,511.3 t C ha-1.Conclusions: In contrast to undisturbed temperate peatlands, human induced disturbances of thenatural elevation gradient of the peatland has resulted in increased heterogeneity of floristic variation and assemblages that are a product of the spatial and temporal patterns of the water table level and the surface wetness across peatlands. Human induced changes in surface hydrology and land use influenced the fuel characteristics of natural vegetation and associated soils, thus influencing wildfire risk, behavior, and the resulting C emissions.

Successional dynamics shape tree diversity in evergreen forests of Côte d’Ivoire, West Africa

Disturbances and successional dynamics shape the composition of tree communities, but data remain scarce for tropical forests of West Africa. We assessed the imprint of past disturbances on the composition of evergreen forests in an Ivorian National Park. We hypothesized that (i) Pioneer indices (PI) based on the relative proportion of pioneer and non-pioneer trees relate to changing floristic composition due to successional dynamics, (ii) local community richness peaks at an intermediate value of PI under the Intermediate Disturbance Hypothesis (IDH) and (iii) early successional communities have higher beta diversity due to erratic founder effects. We performed a Correspondence Analysis of tree composition of 38 plots and examined how the main components of floristic variation related to environmental factors and PI. In addition, we tested the relationship between PI, local richness and beta diversity. The variation of PI better explained the main components of floristic variation than abiotic environmental variation, supporting a primary role of successional dynamics in shaping tree communities. We found a peak of richness at intermediate values of PI, supporting the IDH, with a mixture and earlier and later-successional species and more even abundances. The communities were very diverse and included many endemics and rare species. The results underline that the composition of early successional forests greatly varies depending on chance colonization events, while more similar old-growth communities are eventually observed after several decades. The findings should guide management practices for forest restoration, and for conservation of endangered species depending on their successional status.

Mapping Floristic Patterns of Trees in Peruvian Amazonia Using Remote Sensing and Machine Learning

Recognition of the spatial variation in tree species composition is a necessary precondition for wise management and conservation of forests. In the Peruvian Amazonia, this goal is not yet achieved mostly because adequate species inventory data has been lacking. The recently started Peruvian national forest inventory (INFFS) is expected to change the situation. Here, we analyzed genus-level variation, summarized through non-metric multidimensional scaling (NMDS), in a set of 157 INFFS inventory plots in lowland to low mountain rain forests (<2000 m above sea level) using Landsat satellite imagery and climatic, edaphic, and elevation data as predictor variables. Genus-level floristic patterns have earlier been found to be indicative of species-level patterns. In correlation tests, the floristic variation of tree genera was most strongly related to Landsat variables and secondly to climatic variables. We used random forest regression, under varying criteria of feature selection and cross-validation, to predict the floristic composition on the basis of Landsat and environmental data. The best model explained >60% of the variation along NMDS axes 1 and 2 and 40% of the variation along NMDS axis 3. We used this model to predict the three NMDS dimensions at a 450-m resolution over all of the Peruvian Amazonia and classified the pixels into 10 floristic classes using k-means classification. An indicator analysis identified statistically significant indicator genera for 8 out of the 10 classes. The results are congruent with earlier studies, suggesting that the approach is robust and can be applied to other tropical regions, which is useful for reducing research gaps and for identifying suitable areas for conservation.

Floristic Variation of Tree Communities and Their Association with Soil Properties in Pulau Jerejak, Penang, Peninsular Malaysia

This study was conducted at Pulau Jerejak, Penang to determine the floristic variation of its tree communities. A 0.5-hectare study plot was established and divided into 11 subplots. A total of 587 trees with diameter at breast height (DBH) of 5 cm and above were measured, identified and recorded. The tree communities comprised of 84 species, 63 genera and 32 families. The Myrtaceae was the most speciose family with 10 recorded species while Syzgium glaucum (Myrtaceae) was the most frequent species. The Myrtaceae recorded the highest density of 306 individuals while Syzgium glaucum (Myrtaceae) had the highest species density of 182 individuals. Total tree basal area (BA) was 21.47 m2/ha and family with the highest BA was Myrtaceae with 5.81 m2/ha while at species level, Syzgium glaucum (Myrtaceae) was the species with the highest total BA in the plot with value of 4.95 m2/ha. The Shannon˗Weiner Diversity Index of tree communities showed a value of 3.60 (H'max = 4.43) and Evenness Index of 0.81 which indicates high uniformity of tree species. The Margalef Richness Index (R') revealed that the tree species richness was 13.02. Myrtaceae had the highest Importance Value of 20.4%. The Canonical Correspondence Analysis (CCA) showed that Diospyros buxifolia (Ebenaceae) and Pouteria malaccensis (Sapotaceae) were strongly correlated to low pH. Dysoxylum cauliflorum (Meliaceae) and Eriobotrya bengalensis (Rosaceae) were correlated to phosphorus (P) and calcium ion (Ca2+), respectively. Therefore, the trees species composition at Pulau Jerejak showed that the biodiversity is high and conservation action should be implemented to protect endangered tree species. Keywords: Floristic variation; Tree communities; Trees composition; Pulau Jerejak; Species diversity

Floristic Variation of Tree Communities In Island Forests of Pulau Tuba and Gunung Raya Forest Reserve, Langkawi

Island forests are among forest habitats that are vulnerable to natural and anthropogenic disturbances, whereby the disturbances would influence the survival of biological species of the ecosystems. Langkawi Archipelago contains many small island forests and rapid development of tourism industry within this archipelago might contribute impacts to the tree flora of the forest communities on the small islands. Hence, in this study the species richness and floristic variation pattern of tree communities of two selected island forests in the Langkawi Archipelago were explored, and data gathered are anticipated to be used for management of island forests in Langkawi. Tree survey was carried out in 10 study plots of 20m x 25m each, at island forests of Pulau Tuba Forest Reserve (PTB) and Gunung Raya Forest Reserve (GRFR), making the total of 20 study plots. All trees with diameter at breast height (dbh) of 5.0 cm and above were enumerated and tree species were identified. Species data were analyzed for diversity and richness using the Shannon and Margalef indices; whilst Detrended Correspondence Analysis (DCA) was used to determine floristic pattern. A total of 1062 trees were recorded from all study plots which comprised of 49 families, 134 genera and 213 tree species. The GRFR exhibited the highest species number of 135 tree species, followed by the PTB (106 tree species). Species accumulation curves showed that the curves were far from reaching the asymptote even when the whole dataset were combined. The DCA ordination diagram clearly grouped the study plots by their geological formation that indicated a gradient of species change in GRFR and PTB sites.

TIME SERIES OF IMAGES TO IMPROVE TREE SPECIES CLASSIFICATION

Tree species classification provides valuable information to forest monitoring and management. The high floristic variation of the tree species appears as a challenging issue in the tree species classification because the vegetation characteristics changes according to the season. To help to monitor this complex environment, the imaging spectroscopy has been largely applied since the development of miniaturized sensors attached to Unmanned Aerial Vehicles (UAV). Considering the seasonal changes in forests and the higher spectral and spatial resolution acquired with sensors attached to UAV, we present the use of time series of images to classify four tree species. The study area is an Atlantic Forest area located in the western part of São Paulo State. Images were acquired in August 2015 and August 2016, generating three data sets of images: only with the image spectra of 2015; only with the image spectra of 2016; with the layer stacking of images from 2015 and 2016. Four tree species were classified using Spectral angle mapper (SAM), Spectral information divergence (SID) and Random Forest (RF). The results showed that SAM and SID caused an overfitting of the data whereas RF showed better results and the use of the layer stacking improved the classification achieving a kappa coefficient of 18.26&amp;thinsp;%.